{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 运行环境"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.8.13\n"
     ]
    }
   ],
   "source": [
    "!python -V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.8.13 (default, Mar 28 2022, 06:59:08) [MSC v.1916 64 bit (AMD64)]'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "sys.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.6.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow\n",
    "\n",
    "tensorflow.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.10.1'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "torch.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using backend: tensorflow.compat.v1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From G:\\Anaconda3\\envs\\py3.8\\lib\\site-packages\\tensorflow\\python\\compat\\v2_compat.py:101: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n",
      "WARNING:tensorflow:From G:\\Anaconda3\\envs\\py3.8\\lib\\site-packages\\deepxde\\nn\\initializers.py:118: The name tf.keras.initializers.he_normal is deprecated. Please use tf.compat.v1.keras.initializers.he_normal instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'1.1.3'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import deepxde\n",
    "\n",
    "deepxde.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ DDEBACKEND=tensorflow.compat.v1 python pde.py\n",
    "\n",
    "$ DDEBACKEND=tensorflow python pde.py\n",
    "\n",
    "$ DDEBACKEND=pytorch python pde.py\n",
    "\n",
    "\n",
    "python -m deepxde.backend.set_default_backend tensorflow.compat.v1\n",
    "\n",
    "python -m deepxde.backend.set_default_backend tensorflow\n",
    "\n",
    "python -m deepxde.backend.set_default_backend pytorch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用tensorflow后端"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**重启**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting the default backend to \"tensorflow\". You can change it in the ~/.deepxde/config.json file or export the DDEBACKEND environment variable. Valid options are: tensorflow.compat.v1, tensorflow, pytorch (all lowercase)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using backend: tensorflow\n",
      "\n",
      "2022-02-01 17:34:41.609565: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2022-02-01 17:34:41.609588: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "E:\\AnacondaInstall\\envs\\py3.6\\lib\\runpy.py:125: RuntimeWarning: 'deepxde.backend.set_default_backend' found in sys.modules after import of package 'deepxde.backend', but prior to execution of 'deepxde.backend.set_default_backend'; this may result in unpredictable behaviour\n",
      "  warn(RuntimeWarning(msg))\n"
     ]
    }
   ],
   "source": [
    "# python -m deepxde.backend.set_default_backend tensorflow.compat.v1\n",
    "\n",
    "!python -m deepxde.backend.set_default_backend tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using backend: tensorflow\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Backend supported: tensorflow.compat.v1, tensorflow, pytorch\n",
    "Documentation: https://deepxde.readthedocs.io/en/latest/demos/poisson.1d.dirichlet.html\n",
    "\"\"\"\n",
    "import deepxde as dde\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# Import tf if using backend tensorflow.compat.v1 or tensorflow\n",
    "from deepxde.backend import tf\n",
    "# Import torch if using backend pytorch\n",
    "# import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pde(x, y):\n",
    "    dy_xx = dde.grad.hessian(y, x)\n",
    "    # Use tf.sin for backend tensorflow.compat.v1 or tensorflow\n",
    "    return -dy_xx - np.pi ** 2 * tf.sin(np.pi * x)\n",
    "    # Use torch.sin for backend pytorch\n",
    "    # return -dy_xx - np.pi ** 2 * torch.sin(np.pi * x)\n",
    "\n",
    "\n",
    "def boundary(x, on_boundary):\n",
    "    return on_boundary\n",
    "\n",
    "\n",
    "def func(x):\n",
    "    return np.sin(np.pi * x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\AnacondaInstall\\envs\\py3.6\\lib\\site-packages\\skopt\\sampler\\sobol.py:250: UserWarning: The balance properties of Sobol' points require n to be a power of 2. 0 points have been previously generated, then: n=0+18=18. \n",
      "  total_n_samples))\n"
     ]
    }
   ],
   "source": [
    "geom = dde.geometry.Interval(-1, 1)\n",
    "bc = dde.DirichletBC(geom, func, boundary)\n",
    "data = dde.data.PDE(geom, pde, bc, 16, 2, solution=func, num_test=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_size = [1] + [50] * 3 + [1]\n",
    "activation = \"tanh\"\n",
    "initializer = \"Glorot uniform\"\n",
    "net = dde.maps.FNN(layer_size, activation, initializer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling model...\n",
      "'compile' took 0.000586 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = dde.Model(data, net)\n",
    "model.compile(\"adam\", lr=0.001, metrics=[\"l2 relative error\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model...\n",
      "\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model._compile_tensorflow.<locals>.outputs_losses at 0x000001BD70932620> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: unsupported operand type(s) for -: 'NoneType' and 'int'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model._compile_tensorflow.<locals>.outputs_losses at 0x000001BD70932620> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: unsupported operand type(s) for -: 'NoneType' and 'int'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING:tensorflow:AutoGraph could not transform <bound method FNN.call of <deepxde.nn.tensorflow.fnn.FNN object at 0x000001BD706BFB38>> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: unsupported operand type(s) for -: 'NoneType' and 'int'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <bound method FNN.call of <deepxde.nn.tensorflow.fnn.FNN object at 0x000001BD706BFB38>> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: unsupported operand type(s) for -: 'NoneType' and 'int'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "Step      Train loss              Test loss               Test metric   \n",
      "0         [4.65e+01, 9.30e-03]    [4.90e+01, 9.30e-03]    [1.06e+00]    \n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model._compile_tensorflow.<locals>.train_step at 0x000001BD70932950> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: unsupported operand type(s) for -: 'NoneType' and 'int'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model._compile_tensorflow.<locals>.train_step at 0x000001BD70932950> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: unsupported operand type(s) for -: 'NoneType' and 'int'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "1000      [9.63e-04, 1.62e-06]    [1.30e-03, 1.62e-06]    [1.52e-03]    \n",
      "2000      [2.12e-03, 6.85e-05]    [2.11e-03, 6.85e-05]    [6.75e-03]    \n",
      "3000      [2.20e-04, 1.14e-05]    [5.15e-04, 1.14e-05]    [2.85e-03]    \n",
      "4000      [3.22e-05, 6.22e-07]    [1.19e-04, 6.22e-07]    [5.51e-04]    \n",
      "5000      [5.01e-04, 6.81e-06]    [6.59e-04, 6.81e-06]    [4.73e-03]    \n",
      "6000      [1.47e-04, 6.41e-06]    [1.30e-04, 6.41e-06]    [1.70e-03]    \n",
      "7000      [3.50e-05, 1.13e-06]    [7.95e-05, 1.13e-06]    [6.81e-04]    \n",
      "8000      [2.16e-05, 2.92e-07]    [9.19e-05, 2.92e-07]    [6.64e-04]    \n",
      "9000      [1.44e-03, 4.61e-05]    [1.55e-03, 4.61e-05]    [4.70e-03]    \n",
      "10000     [2.08e-04, 4.23e-06]    [2.56e-04, 4.23e-06]    [2.00e-03]    \n",
      "\n",
      "Best model at step 8000:\n",
      "  train loss: 2.19e-05\n",
      "  test loss: 9.22e-05\n",
      "  test metric: [6.64e-04]\n",
      "\n",
      "'train' took 5.127495 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "losshistory, train_state = model.train(epochs=10000)\n",
    "# Optional: Save the model during training.\n",
    "# checkpointer = dde.callbacks.ModelCheckpoint(\n",
    "#     \"model/model\", verbose=1, save_better_only=True\n",
    "# )\n",
    "# Optional: Save the movie of the network solution during training.\n",
    "# ImageMagick (https://imagemagick.org/) is required to generate the movie.\n",
    "# movie = dde.callbacks.MovieDumper(\n",
    "#     \"model/movie\", [-1], [1], period=100, save_spectrum=True, y_reference=func\n",
    "# )\n",
    "# losshistory, train_state = model.train(epochs=10000, callbacks=[checkpointer, movie])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving loss history to E:\\noetbook\\毕业论文\\loss.dat ...\n",
      "Saving training data to E:\\noetbook\\毕业论文\\train.dat ...\n",
      "Saving test data to E:\\noetbook\\毕业论文\\test.dat ...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dde.saveplot(losshistory, train_state, issave=True, isplot=True)\n",
    "\n",
    "# Optional: Restore the saved model with the smallest training loss\n",
    "# model.restore(f\"model/model-{train_state.best_step}.ckpt\", verbose=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function Model.predict.<locals>.op at 0x000001BD79FCA6A8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: unsupported operand type(s) for -: 'NoneType' and 'int'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.predict.<locals>.op at 0x000001BD79FCA6A8> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: unsupported operand type(s) for -: 'NoneType' and 'int'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot PDE residual\n",
    "x = geom.uniform_points(1000, True)\n",
    "y = model.predict(x, operator=pde)\n",
    "plt.figure()\n",
    "plt.plot(x, y)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"PDE residual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用pytorch后端"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**重启**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting the default backend to \"pytorch\". You can change it in the ~/.deepxde/config.json file or export the DDEBACKEND environment variable. Valid options are: tensorflow.compat.v1, tensorflow, pytorch (all lowercase)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using backend: tensorflow\n",
      "\n",
      "2022-02-01 17:23:55.164210: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found\n",
      "2022-02-01 17:23:55.164250: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n",
      "E:\\AnacondaInstall\\envs\\py3.6\\lib\\runpy.py:125: RuntimeWarning: 'deepxde.backend.set_default_backend' found in sys.modules after import of package 'deepxde.backend', but prior to execution of 'deepxde.backend.set_default_backend'; this may result in unpredictable behaviour\n",
      "  warn(RuntimeWarning(msg))\n"
     ]
    }
   ],
   "source": [
    "!python -m deepxde.backend.set_default_backend pytorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using backend: pytorch\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Backend supported: tensorflow.compat.v1, tensorflow, pytorch\n",
    "\n",
    "Documentation: https://deepxde.readthedocs.io/en/latest/demos/poisson.1d.dirichlet.html\n",
    "\"\"\"\n",
    "import deepxde as dde\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# Import tf if using backend tensorflow.compat.v1 or tensorflow\n",
    "# from deepxde.backend import tf\n",
    "# Import torch if using backend pytorch\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pde(x, y):\n",
    "    dy_xx = dde.grad.hessian(y, x)\n",
    "    # Use tf.sin for backend tensorflow.compat.v1 or tensorflow\n",
    "    # return -dy_xx - np.pi ** 2 * tf.sin(np.pi * x)\n",
    "    # Use torch.sin for backend pytorch\n",
    "    return -dy_xx - np.pi ** 2 * torch.sin(np.pi * x)\n",
    "\n",
    "\n",
    "def boundary(x, on_boundary):\n",
    "    return on_boundary\n",
    "\n",
    "\n",
    "def func(x):\n",
    "    return np.sin(np.pi * x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\AnacondaInstall\\envs\\py3.6\\lib\\site-packages\\skopt\\sampler\\sobol.py:250: UserWarning: The balance properties of Sobol' points require n to be a power of 2. 0 points have been previously generated, then: n=0+18=18. \n",
      "  total_n_samples))\n"
     ]
    }
   ],
   "source": [
    "geom = dde.geometry.Interval(-1, 1)\n",
    "bc = dde.DirichletBC(geom, func, boundary)\n",
    "data = dde.data.PDE(geom, pde, bc, 16, 2, solution=func, num_test=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_size = [1] + [50] * 3 + [1]\n",
    "activation = \"tanh\"\n",
    "initializer = \"Glorot uniform\"\n",
    "net = dde.maps.FNN(layer_size, activation, initializer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling model...\n",
      "'compile' took 0.000204 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = dde.Model(data, net)\n",
    "model.compile(\"adam\", lr=0.001, metrics=[\"l2 relative error\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model...\n",
      "\n",
      "Step      Train loss              Test loss               Test metric   \n",
      "0         [4.54e+01, 1.54e-01]    [4.79e+01, 1.54e-01]    [7.66e-01]    \n",
      "1000      [4.24e-04, 3.39e-07]    [8.30e-04, 3.39e-07]    [2.31e-03]    \n",
      "2000      [1.96e-05, 3.64e-08]    [1.44e-04, 3.64e-08]    [2.66e-04]    \n",
      "3000      [1.15e-05, 1.06e-10]    [1.15e-04, 1.06e-10]    [1.42e-04]    \n",
      "4000      [1.90e-05, 6.34e-07]    [1.16e-04, 6.34e-07]    [9.26e-04]    \n",
      "5000      [8.13e-06, 6.59e-09]    [1.00e-04, 6.59e-09]    [1.52e-04]    \n",
      "6000      [2.40e-04, 1.72e-05]    [3.64e-04, 1.72e-05]    [4.87e-03]    \n",
      "7000      [7.77e-06, 6.85e-09]    [1.02e-04, 6.85e-09]    [2.19e-04]    \n",
      "8000      [2.93e-05, 4.07e-07]    [1.07e-04, 4.07e-07]    [7.86e-04]    \n",
      "9000      [1.77e-05, 2.46e-06]    [8.71e-05, 2.46e-06]    [1.66e-03]    \n",
      "10000     [9.55e-04, 1.83e-04]    [5.80e-04, 1.83e-04]    [1.29e-02]    \n",
      "\n",
      "Best model at step 7000:\n",
      "  train loss: 7.78e-06\n",
      "  test loss: 1.02e-04\n",
      "  test metric: [2.19e-04]\n",
      "\n",
      "'train' took 40.273321 s\n",
      "\n"
     ]
    }
   ],
   "source": [
    "losshistory, train_state = model.train(epochs=10000)\n",
    "# Optional: Save the model during training.\n",
    "# checkpointer = dde.callbacks.ModelCheckpoint(\n",
    "#     \"model/model\", verbose=1, save_better_only=True\n",
    "# )\n",
    "# Optional: Save the movie of the network solution during training.\n",
    "# ImageMagick (https://imagemagick.org/) is required to generate the movie.\n",
    "# movie = dde.callbacks.MovieDumper(\n",
    "#     \"model/movie\", [-1], [1], period=100, save_spectrum=True, y_reference=func\n",
    "# )\n",
    "# losshistory, train_state = model.train(epochs=10000, callbacks=[checkpointer, movie])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving loss history to E:\\noetbook\\毕业论文\\loss.dat ...\n",
      "Saving training data to E:\\noetbook\\毕业论文\\train.dat ...\n",
      "Saving test data to E:\\noetbook\\毕业论文\\test.dat ...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dde.saveplot(losshistory, train_state, issave=True, isplot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Optional: Restore the saved model with the smallest training loss\n",
    "# model.restore(f\"model/model-{train_state.best_step}.ckpt\", verbose=1)\n",
    "# Plot PDE residual\n",
    "x = geom.uniform_points(1000, True)\n",
    "y = model.predict(x, operator=pde)\n",
    "plt.figure()\n",
    "plt.plot(x, y)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"PDE residual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py3.8",
   "language": "python",
   "name": "py3.8"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
